Skip to main content
SpringerLink
Log in

Epileptiform Activity Detection and Classification Algorithms of Rats with Post-traumatic Epilepsy

  • Applied Problems
  • Published:
Pattern Recognition and Image Analysis Aims and scope Submit manuscript

Abstract

In this paper, the problem of epileptiform activity in EEG of rats before and after Traumatic Brain Injury is considered. Experts in neurology performed a manual markup of signals as Epileptiform Discharges and Sleep Spindles. A proprietary Event Detection Algorithm based on time-frequency analysis of wavelet spectrograms was created. Feature space from PSD and Frequency of a detected event was created, and each feature was assessed for importance of epileptic activity prediction. Resulted predictors were used for training logistic regression model, which estimated features weights in probability of epilepsy function. Validation of proposed model was done on Monte-Carlo simulation of cross-validations. It was showed that the accuracy of prediction is around 80%. Proposed Epilepsy Prediction Model, as well as Event Detection Algorithm, can be applied to identification of epileptiform activity in long term records of rats and analysis of disease dynamics.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. A. Aarabi, F. Wallois, and R. Grebe, “Automated neonatal seizure detection: a multistage classification system through feature selection based on relevance and redundancy analysis,” Clin. Neurophysiol. 117 (2), 328–340 (2006).

    Article  Google Scholar 

  2. M.E. Saab and J. Gotman, “A system to detect the onset of epileptic seizures in scalp EEG,” Clin. Neurophysiol. 116 (2), 427–442 (2005).

    Article  Google Scholar 

  3. S. Blanco, S. Kochen, O.A. Rosso, and P. Salgado, “Applying time-frequency analysis to seizure EEG activity,” IEEE Eng. Med. Biol. Mag. 16 (1), 64–71 (1997).

    Article  Google Scholar 

  4. D. Flanagan, R. Agarwal, Y.H. Wang, and J. Gotman, “Improvement in the performance of automated spike detection using dipole source features for artefact rejection,” Clin. Neurophysiol. 114 (1), 38–49 (2003).

    Article  Google Scholar 

  5. B.R. Greene, S. Faul, W.P. Marnane, G. Lightbody, I. Korotchikova, and G.B. Boylan, “A comparison of quantitative EEG features for neonatal seizure detection,” Clin. Neurophysiol. 119 (6), 248–1261 (2008).

    Article  Google Scholar 

  6. A. Subasi, “Automatic detection of epileptic seizure using dynamic fuzzy neural networks,” Expert Syst. Appl. 31 (2), 320–328 (2006).

    Article  MathSciNet  Google Scholar 

  7. S. C. Warby, S. L. Wendt, P. Welinder, E. G. S. Munk, O. Carrillo, H. B. D. Sorensen, P. Jennum, P. E. Peppard, P. Perona, and E. Mignot, “Sleep-spindle detection: crowdsourcing and evaluating performance of experts, non-experts and automated methods,” Nature Methods 11 (4), 385–392 (2014).

    Article  Google Scholar 

  8. A. A. Dingle, R. D. Jones, G. J. Carroll, and W. R. Fright, “A multistage system to detect epileptiform activity in the EEG,” IEEE Trans. Biomed. Eng. 40 (12), 1260–1268 (1993).

    Article  Google Scholar 

  9. N. Päivinen, S. Lammi, A. Pitkänen, J. Nissinen, M. Penttonen, and T. Grönfors, “Epileptic seizure detection: A nonlinear viewpoint,” Comput. Meth. Progr. Biomed. 79 (2), 151–159 (2005).

    Article  Google Scholar 

  10. A. Aarabi, R. Fazel-Rezai, and Y. Aghakhani, “EEG seizure prediction: Measures and challenges,” in Proc. 2009 31st Annual Int. Conf. of the IEEE Engineering in Medicine and Biology Society (EMBC) (IEEE, 2009), pp. 1864–1867.

    Chapter  Google Scholar 

  11. S. V. Kabadi, G. D. Hilton, B. A. Stoica, D. N. Zapple, and A. I. Faden, “Fluid-percussion-induced traumatic brain injury model in rats,” Nat. Protoc. 5 (9), 1552–1563 (2010).

    Article  Google Scholar 

  12. E. C. Ifeachor and B. W. Jervis, Digital Signal Processing: A Practical Approach, 2nd ed. (Pearson Education, Harlow, 2002).

    Google Scholar 

  13. J. Van Zaen, M. M. Murray, R. A. Meuli, and J.-M. Vesin, “Adaptive filtering methods for identifying cross-frequency couplings in human EEG,” PLOS ONE 8 (4), e60513 (2013).

    Article  Google Scholar 

  14. M. H. Libenson, Practical Approach to Electroencephalography E-Book (Elsevier Health Sciences, 2012).

    Google Scholar 

  15. B. Moslem, B. Karlsson, M.O. Diab, M. Khalil, and C. Marque, “Classification performance of the frequency-related parameters derived from uterine EMG signals,” in Proc 2011 33rd Annual Int. Conf. of the IEEE Engineering in Medicine and Biology Society (EMBC) (IEEE, 2011), pp. 3371–3374.

    Chapter  Google Scholar 

  16. W. L. Maner and R. E. Garfield, “Identification of human term and preterm labor using artificial neural networks on uterine electromyography data,” Ann. Biomed. Eng. 35 (3), 465–473 (2007).

    Article  Google Scholar 

  17. M. Hassan, J. Terrien, C. Marque, and B. Karlsson, “Comparison between approximate entropy, correntropy and time reversibility: Application to uterine electromyogram signals,” Med. Eng. Phys. 33 (8), 980–986 (2011).

    Article  Google Scholar 

  18. C. Buhimschi, M. B. Boyle, G. R. Saade, and R. E. Garfield, “Uterine activity during pregnancy and labor assessed by simultaneous recordings from the myometrium and abdominal surface in the rat,” Am. J. Obstet. Gynecol. 178 (4), 811–822 (1998).

    Article  Google Scholar 

  19. N. Kannathal, M. L. Choo, U. R. Acharya, and P. K. Sadasivan, “Entropies for detection of epilepsy in EEG,” Comput. Methods Programs Biomed. 80 (3), 187–194 (2005).

    Article  Google Scholar 

  20. A. A. Abdul-Latif, I. Cosic, D. K. Kumar, B. Polus, and C. Da Costa, “Power changes of EEG signals associated with muscle fatigue: the root mean square analysis of EEG bands,” in Proc. of the 2004 Intelligent Sensors, Sensor Networks, and Information Processing Conf. (IEEE, 2004), pp. 531–534.

    Chapter  Google Scholar 

  21. I. Omerhodzic, S. Avdakovic, A. Nuhanovic, and K. Dizdarevic, “Energy distribution of EEG signals: EEG signal wavelet-neural network classifier,” Int. J. Med., Health, Biomed., Bioeng. Pharm. Eng. 4 (1), 35–40 (2010).

    Google Scholar 

  22. W. L. Maner, R. E. Garfield, H. Maul, G. Olson, and G. Saade, “Predicting term and preterm delivery with transabdominal uterine electromyography,” Obstet. Gynecol. 101 (6), 1254–1260 (2003).

    Google Scholar 

  23. P. Goupillaud, A. Grossmann, and J. Morlet, “Cycleoctave and related transforms in seismic signal analysis,” Geoexploration 23 (1), 85–102 (1984).

    Article  Google Scholar 

  24. K. Obukhov, I. Kershner, I. Komol’tsev, I. Maluta, Yu. Obukhov, A. Manolova, and N. Gulyaeva, “An approach for EEG of post traumatic sleep spindles and epilepsy seizures detection and classification in rats,” Pattern Recogn. Image Anal. 27 (1), 114–121 (2017).

    Article  Google Scholar 

  25. S. Menard, Applied Logistic Regression Analysis, 2nd. ed. (Sage, Thousand Oaks, CA, 2002).

    Book  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Moscow Institute of Physics and Technology, Dolgoprudny, Moscow oblast, Russia

    K. Obukhov

  2. Kotelnikov’ Institute of Radio-Engineering and Electronics RAS, Moscow, Russia

    I. Kersher & Yu. Obukhov

  3. Institute of Higher Nervous Activity and Neurophysiology of RAS, Moscow, Russia

    I. Komoltsev

Authors
  1. K. Obukhov
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. I. Kersher
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. I. Komoltsev
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yu. Obukhov
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to K. Obukhov.

Additional information

The article is published in the original.

Konstantin Obukhov. Born in 1993. PhD student at Moscow Institute of Physics and Technology. Author of 23 scientific publications. Area of interest: information technologies, machine learning, biomedical signal processing.

Ivan Kershner. Born in 1992. Graduated from Moscow Institute of Physics and Technology in 2010. PhD student at Kotel’nikov Institute of Radio-Engineering and Electronics RAS. Author of 15 scientific publications. Area of interest: signal and image processing, information technologies.

Il’ya Komoltsev. Born in 1991. Graduated from Pirogov Russian National Research Medical University in 2015. Junior research fellow at Institute of Higher Nervous Activity and Neurophysiology, RAS. Area of interest: neurophysiology, EEG, traumatic brain injury.

Yurii Obukhov. Born in 1950. Graduated from Moscow Institute of Physics and Technology in 1974. Since 1982 holds PhD and 1992–Doctor of Sciences in physics and applied mathematics. Chief scientific officer and a Head of laboratory at Kotel’nikov Institute of Radio-Engineering and Electronics RAS. Author of more than 150 scientific publications. Area of interest: signal processing and analysis, information systems.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Obukhov, K., Kersher, I., Komoltsev, I. et al. Epileptiform Activity Detection and Classification Algorithms of Rats with Post-traumatic Epilepsy. Pattern Recognit. Image Anal. 28, 346–353 (2018). https://doi.org/10.1134/S1054661818020153

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1134/S1054661818020153

Keywords

Search

Navigation